import datetime
import json
import logging
import os
import shutil
import sys
import uuid
from json import JSONDecodeError
from pathlib import Path
from time import sleep
import openai
import pandas as pd
import pinecone
import streamlit as st
from annotated_text import annotation
from haystack import Document
from haystack.document_stores import PineconeDocumentStore
from haystack.nodes import (
DocxToTextConverter,
EmbeddingRetriever,
FARMReader,
FileTypeClassifier,
PDFToTextConverter,
PreProcessor,
TextConverter,
)
from haystack.pipelines import ExtractiveQAPipeline, Pipeline
from markdown import markdown
from sentence_transformers import SentenceTransformer
from tqdm.auto import tqdm
# get API key from top-right dropdown on OpenAI website
openai.api_key = st.secrets["OPENAI_API_KEY"]
index_name = "openai-ada-002-index"
# connect to pinecone environment
pinecone.init(api_key=st.secrets["pinecone_apikey"], environment="us-east1-gcp")
embed_model = "text-embedding-ada-002"
preprocessor = PreProcessor(
clean_empty_lines=True,
clean_whitespace=True,
clean_header_footer=False,
split_by="word",
split_length=200,
split_respect_sentence_boundary=True,
)
file_type_classifier = FileTypeClassifier()
text_converter = TextConverter()
pdf_converter = PDFToTextConverter()
docx_converter = DocxToTextConverter()
# check if the abstractive-question-answering index exists
if index_name not in pinecone.list_indexes():
# delete the current index and create the new index if it does not exist
for delete_index in pinecone.list_indexes():
pinecone.delete_index(delete_index)
pinecone.create_index(index_name, dimension=1536, metric="cosine")
# connect to abstractive-question-answering index we created
index = pinecone.Index(index_name)
FILE_UPLOAD_PATH = "./data/uploads/"
os.makedirs(FILE_UPLOAD_PATH, exist_ok=True)
limit = 3750
def retrieve(query):
res = openai.Embedding.create(input=[query], engine=embed_model)
# retrieve from Pinecone
xq = res["data"][0]["embedding"]
# get relevant contexts
res = index.query(xq, top_k=3, include_metadata=True)
contexts = [x["metadata"].get("text", "") for x in res["matches"]]
# build our prompt with the retrieved contexts included
prompt_start = "You are question-answering chatbot by Wellous. Answer the question based on the context below. Your answer should be concise. If you are unsure or have no clear answer, respond with \"I don't know\" \n\n" + "Context:\n"
prompt_end = f"\n\nQuestion: {query} (concise)\nAnswer:"
# append contexts until hitting limit
for i in range(1, len(contexts)):
if len("\n\n---\n\n".join(contexts[:i])) >= limit:
prompt = prompt_start + "\n\n---\n\n".join(contexts[: i - 1]) + prompt_end
break
elif i == len(contexts) - 1:
prompt = prompt_start + "\n\n---\n\n".join(contexts) + prompt_end
return prompt, contexts
# first let's make it simpler to get answers
def complete(prompt):
# query text-davinci-003
print('prompt',prompt)
res = openai.Completion.create(
engine="text-davinci-003",
prompt=prompt,
temperature=0,
max_tokens=400,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
stop=None,
)
return res["choices"][0]["text"].strip()
def query(question, top_k_reader, top_k_retriever):
# first we retrieve relevant items from Pinecone
query_with_contexts, contexts = retrieve(question)
return complete(query_with_contexts), contexts
indexing_pipeline_with_classification = Pipeline()
indexing_pipeline_with_classification.add_node(
component=file_type_classifier, name="FileTypeClassifier", inputs=["File"]
)
indexing_pipeline_with_classification.add_node(
component=text_converter, name="TextConverter", inputs=["FileTypeClassifier.output_1"]
)
indexing_pipeline_with_classification.add_node(
component=pdf_converter, name="PdfConverter", inputs=["FileTypeClassifier.output_2"]
)
indexing_pipeline_with_classification.add_node(
component=docx_converter, name="DocxConverter", inputs=["FileTypeClassifier.output_4"]
)
indexing_pipeline_with_classification.add_node(
component=preprocessor,
name="Preprocessor",
inputs=["TextConverter", "PdfConverter", "DocxConverter"],
)
def set_state_if_absent(key, value):
if key not in st.session_state:
st.session_state[key] = value
# Adjust to a question that you would like users to see in the search bar when they load the UI:
DEFAULT_QUESTION_AT_STARTUP = os.getenv(
"DEFAULT_QUESTION_AT_STARTUP", "What is Bio-lingzhi?"
)
DEFAULT_ANSWER_AT_STARTUP = os.getenv(
"DEFAULT_ANSWER_AT_STARTUP",
"-",
)
# Sliders
DEFAULT_DOCS_FROM_RETRIEVER = int(os.getenv("DEFAULT_DOCS_FROM_RETRIEVER", "3"))
DEFAULT_NUMBER_OF_ANSWERS = int(os.getenv("DEFAULT_NUMBER_OF_ANSWERS", "3"))
st.set_page_config(
page_title="GPT3 and Langchain Demo"
)
# Persistent state
set_state_if_absent("question", DEFAULT_QUESTION_AT_STARTUP)
set_state_if_absent("answer", DEFAULT_ANSWER_AT_STARTUP)
set_state_if_absent("results", None)
# Small callback to reset the interface in case the text of the question changes
def reset_results(*args):
st.session_state.answer = None
st.session_state.results = None
st.session_state.raw_json = None
# Title
st.write("# GPT3 and Langchain Demo")
# st.markdown(
# """
# This demo takes its data from the documents uploaded to the Pinecone index through this app. \n
# Ask any question from the uploaded documents and Pinecone will retrieve the context for answers and GPT3 will answer them using the retrieved context. \n
# *Note: do not use keywords, but full-fledged questions.* The demo is not optimized to deal with keyword queries and might misunderstand you.
# """,
# unsafe_allow_html=True,
# )
# Sidebar
# st.sidebar.header("Options")
# st.sidebar.write("## File Upload:")
# data_files = st.sidebar.file_uploader(
# "upload", type=["pdf", "txt", "docx"], accept_multiple_files=True, label_visibility="hidden"
# )
# print("data_files",data_files)
ALL_FILES = []
META_DATA = []
# for data_file in data_files:
# # Upload file
# if data_file:
# file_path = Path(FILE_UPLOAD_PATH) / f"{uuid.uuid4().hex}_{data_file.name}"
# print("file_path",file_path)
# print("data_file",data_file)
# print("data_file.getbuffer()",data_file.getbuffer())
# with open(file_path, "wb") as f:
# f.write(data_file.getbuffer())
# ALL_FILES.append(file_path)
# st.sidebar.write(str(data_file.name) + " ✅ ")
# META_DATA.append({"filename": data_file.name})
text_file = 'wellous_products.txt'
file_path = "./" f"{text_file}"
print("file_path",file_path)
ALL_FILES.append(file_path)
META_DATA.append({"filename": text_file})
print("ALL_FILES",ALL_FILES)
print("META_DATA",META_DATA)
# if len(ALL_FILES) > 0:
# document_store.update_embeddings(retriever, update_existing_embeddings=False)
docs = indexing_pipeline_with_classification.run(file_paths=ALL_FILES, meta=META_DATA)[
"documents"
]
index_name = "qa_demo"
# we will use batches of 64
batch_size = 100
# docs = docs['documents']
with st.spinner("🧠 Performing indexing of uploaded documents... \n "):
for i in range(0, len(docs), batch_size):
# find end of batch
i_end = min(i + batch_size, len(docs))
# extract batch
batch = [doc.content for doc in docs[i:i_end]]
# generate embeddings for batch
try:
res = openai.Embedding.create(input=batch, engine=embed_model)
except Exception as e:
done = False
count = 0
while not done and count < 5:
sleep(5)
try:
res = openai.Embedding.create(input=batch, engine=embed_model)
done = True
except:
count += 1
pass
if count >= 5:
res = []
st.error(f"🐞 File indexing failed{str(e)}")
if len(res) > 0:
embeds = [record["embedding"] for record in res["data"]]
# get metadata
meta = []
for doc in docs[i:i_end]:
meta_dict = doc.meta
meta_dict["text"] = doc.content
meta.append(meta_dict)
# create unique IDs
ids = [doc.id for doc in docs[i:i_end]]
# add all to upsert list
to_upsert = list(zip(ids, embeds, meta))
# upsert/insert these records to pinecone
_ = index.upsert(vectors=to_upsert)
# top_k_reader = st.sidebar.slider(
# "Max. number of answers",
# min_value=1,
# max_value=10,
# value=DEFAULT_NUMBER_OF_ANSWERS,
# step=1,
# on_change=reset_results,
# )
# top_k_retriever = st.sidebar.slider(
# "Max. number of documents from retriever",
# min_value=1,
# max_value=10,
# value=DEFAULT_DOCS_FROM_RETRIEVER,
# step=1,
# on_change=reset_results,
# )
# data_files = st.file_uploader(
# "upload", type=["csv"], accept_multiple_files=True, label_visibility="hidden"
# )
# for data_file in data_files:
# # Upload file
# if data_file:
# raw_json = upload_doc(data_file)
question = st.text_input(
value=st.session_state.question,
max_chars=100,
on_change=reset_results,
label="question",
label_visibility="hidden",
)
col1, col2 = st.columns(2)
col1.markdown("", unsafe_allow_html=True)
col2.markdown("", unsafe_allow_html=True)
# Run button
run_pressed = col1.button("Send")
if run_pressed:
run_query = run_pressed or question != st.session_state.question
# Get results for query
if run_query and question:
reset_results()
st.session_state.question = question
with st.spinner("🧠 Performing neural search on documents... \n "):
try:
st.session_state.results = query(question, top_k_reader=None, top_k_retriever=None)
except JSONDecodeError as je:
st.error(
"👓 An error occurred reading the results. Is the document store working?"
)
except Exception as e:
logging.exception(e)
if "The server is busy processing requests" in str(e) or "503" in str(e):
st.error("🧑🌾 All our workers are busy! Try again later.")
else:
st.error(f"🐞 An error occurred during the request. {str(e)}")
if st.session_state.results:
st.write("## Results:")
result, contexts = st.session_state.results
# answer, context = result.answer, result.context
# start_idx = context.find(answer)
# end_idx = start_idx + len(answer)
# Hack due to this bug: https://github.com/streamlit/streamlit/issues/3190
try:
# source = f"[{result.meta['Title']}]({result.meta['link']})"
# st.write(
# markdown(f'**Source:** {source} \n {context[:start_idx] } {str(annotation(answer, "ANSWER", "#8ef"))} {context[end_idx:]} \n '),
# unsafe_allow_html=True,
# )
all_contexts = '\n'.join(contexts)
st.write(markdown(f"Answer: \n {result} \n"),
unsafe_allow_html=True,
)
except:
# filename = result.meta.get('filename', "")
# st.write(
# markdown(f'From file: {filename} \n {context[:start_idx] } {str(annotation(answer, "ANSWER", "#8ef"))} {context[end_idx:]} \n '),
# unsafe_allow_html=True,
# )
st.write(
markdown(f"Answer: {result}"),
unsafe_allow_html=True,
)